Methods and apparatuses for processing images of particles in a fluid sample are well known. For example, U.S. Pat. Nos. 4,338,024, 4,393,466, 4,667,335 and 4,612,614 describe apparatuses for analyzing biological particles. Such biological particle analysis apparatuses can automatically—i.e., without human intervention—determine characteristics such as color, size, and brightness of particles in a fluid sample. Moreover, based on the determined characteristics, these apparatuses can categorize each particle into one of many classes and calculate the concentration of each particle type (i.e., particle class). This automatic sample analysis and concentration determination process is referred to as Auto-Particle Recognition (APR).
The classification and calculation results can be displayed in the manner disclosed in U.S. Pat. No. 5,822,447. Namely, a plurality of optical frames are taken, wherein each frame is a picture of a portion of the sample. Preferably, the frames represent different portions of the sample. A frame is made of one or more “patches” of images, with each patch containing at least one particle image. Patch recognition can be implemented according to U.S. Patent Application Publication 2004/0136593. The patches are classified into one of a plurality of classes based on the images they contain, and the classes are usually characterized by one or more visually discernible characteristics. In some embodiments, if a patch contains more than one discernable particle image, the particle images could be classified separately. In other embodiments, the image of the more predominant particle is used to classify the patch. Neural network technology can be utilized in the automated classification process, such as disclosed in U.S. Patent Application Publication 2004/0126008. After the classification, the concentrations of each class of particles are determined.
The patches extracted from the frames can displayed on a graphical user interface (e.g., a computer monitor), preferably in an ordered array by classification. The number of particles within each class, or any parameter derived therefrom (e.g., a percentage of the total number of particles), may be displayed. The APR process determines the concentration (i.e. otherwise referred to as the count, which is the number of particles per unit volume of the specimen) of each particle type (i.e., particle class) based on this classification. Then, an operator can manually review the APR classification results and correct any errors. During the manual review process, the operator may pull a misclassified particle out of one class and add it to another class.
One application for APR is counting red blood cells (RBCs) and white blood cells (WBCs) (otherwise known as lymphocytes) from a spinal fluid specimen. The problem is that for some APR systems, it can be difficult to accurately discriminate between and quantify RBCs and WBCs. There is a need for a system and method for improved particle classification.